What are bottleneck features?
In the blog post Building powerful image classification models using very little data, bottleneck features are mentioned. What are the bottleneck features?


It's clearly written in the link you gave the "bottleneck features" from the VGG16 model: the last activation maps before the fully-connected layers.


  Do they change with the architecture that is used?


Sure. The author most likely used a pre-trained model (trained on a large data and now used only as a feature extractor)  


  Are they the final output of convolutional layers before the fully-connected layer?


Yes.  


  Why are they called so?


Given the input size to VGG, the feature maps of HxW dimensions are getting twice smaller after every max-pool operation. HxW is the smallest on the last convolutional layer.